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Record W2055264239 · doi:10.1309/ajcpqct94sferlni

Educating Medical Students in Laboratory Medicine

2010· article· en· W2055264239 on OpenAlexaff
Brian R. Smith, Maria E. Aguero‐Rosenfeld, John Anastasi, Beverly W. Baron, Anders H. Berg, Jay L. Bock, Sheldon Campbell, Kendall P. Crookston, Robert L. Fitzgerald, Mark Fung, Richard L. Haspel, John Greg Howe, Jeffrey S. Jhang, Malek Kamoun, Susan M. Koethe, Matthew D. Krasowski, Marie L. Landry, Marisa B. Marques, Henry M. Rinder, William L. Roberts, William E. Schreiber, Steven L. Spitalnik, Christopher A. Tormey, Yan Wu

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2010
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculumMedical educationMedical laboratoryMedicineAlternative medicineHealth careMedical schoolPsychologyPathologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

As the 100th anniversary of the Flexner report nears, medical student education is being reviewed at many levels. One area of concern, expressed in recent reports from some national health care organizations, is the adequacy of training in the discipline of laboratory medicine (also termed clinical pathology). The Academy of Clinical Laboratory Physicians and Scientists appointed an ad hoc committee to review this topic and to develop a suggested curriculum, which was subsequently forwarded to the entire membership for review. The proposed medical student laboratory medicine curriculum defines goals and objectives for training, provides guidelines for instructional methods, and gives examples of how outcomes can be assessed. This curriculum is presented as a potentially helpful outline for use by medical school faculty and curriculum committees.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0020.009
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.512
Teacher spread0.471 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations87
Published2010
Admission routes1
Has abstractyes

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